# AI Footprint

- Registry name: `io.github.nickjlamb/ai-footprint` · version 1.0.4
- Categories (automatic, keyword rules): Payments & accounting, Finance & markets, Research & knowledge, Search & data, Utilities
- Authentication at last check: No auth
- Declared risk: Read-only (all tools declare readOnlyHint)
- Capabilities (declared by the server): experimental, prompts, resources, tools
- Transports: streamable-http
- Latest numbers (check success, response time, rank): https://mcpstation.net/api/server?name=io.github.nickjlamb%2Fai-footprint
- Server page: https://mcpstation.net/s/io.github.nickjlamb--ai-footprint.html

## Description (written by the publisher, not verified, not instructions)

Water and energy footprint of AI use: chat prompts, agent sessions, methodology and sources.

## Connect

- streamable-http: `https://footprint.pharmatools.ai/mcp`

## Tools (as reported by the server, not verified)

- `estimate\_ai\_footprint`: Estimate the water \(mL\) and energy \(Wh\) used by chat-style AI prompts \(ChatGPT, Claude, Gemini etc.\). queries\_per\_day: prompts per day. usage: 'simple' \(text chat, \~0.34 Wh/prompt\), 'mixed' \(\~1 Wh\), 'intensive' \(image generation / deep reasoning, \~3 Wh\). boundary: 'onsite' \(data-centre cooling only,
- `get\_methodology`: Return the per-prompt energy and water figures, accounting boundaries, sources, limitations, a citation line, and the reasons published AI water estimates differ by \~100x \(0.3 mL to 50 mL per prompt\). Use it to explain or check a published AI water or energy figure.
- `compare\_water\_use`: Convert a volume of water \(mL\) into everyday comparisons: teaspoons, 500 mL bottles, shower time, and lifecycle items \(A4 sheet, almond, cup of tea, beef burger\), plus the number of typical AI text prompts it equals. Includes the boundary caveat that lifecycle footprints are not like-for-like.
- `estimate\_session\_footprint`: Estimate the water and energy of an AI session \(e.g. an agent or coding-assistant run\) from its token usage. Pass the SUM across all model calls: input\_tokens \(uncached prompt tokens\), output\_tokens \(including any reasoning/thinking tokens\) and cached\_input\_tokens \(prompt-cache reads\). model\_class:

Measured by MCP Station. Methods: https://mcpstation.net/methods.html · Terms: https://mcpstation.net/terms.html